Land use land cover changes have to a great extent changed the world's landscapes, rebuilding environments and what they provide to humans during the time spent supporting the rising population across the globe. The drivers of these changes vary from location to location causing varying effects that challenge the essential design and the functioning capacity of the land quality with flowing consequences to land quality. A cross sectional descriptive design together with a longitudinal design were used. A random sampling was used to obtain a sample size of 384 from a study population of 113,476. Based on agriculture, forest land, bare land and built up land categories, the study classified Landsat images through supervised classification algorithm then applied post - classification comparison change detection to measure land use land cover percentage area change over time. Primary data was collected through interviews and discussions with key informants, field observations, and questionnaires administered to individual households in Khwisero Sub County. Secondary data involved downloading Landsat images (Landsat 7, 8 and 9; 30-meter multispectral), summaries and citation of other works carried in journals articles, original documents, annual reports, development plans and internet. Quantitative data analysis involved measures of central tendency and measures of dispersion (SPSS) and analysis of variance (ANOVA). Qualitative data was analysed by organizing and grouping the arising issues into various categories relevant to the study. Land use land cover classification of the study area realized four land use land cover classes of agriculture 81.34 km², forest 52.75 km², built up 8.86 km² and bare land 2.65 km² for the study area as at 2023. Change detection noted that agricultural land use has been reducing from 2002 to 2023, built up has been increasing from 2002 to 2023, bare land increased between 2002 to 2012 but decreased between 2012 and 2023 while forests reduced between 2002 to 2012 but increased between 2012 to 2023   Accuracy assessment for the land use land cover classes for 2002 was 85.45% with a Kappa coefficient of 0.756, 2012 was 83.64% with a Kappa coefficient of 0.5454 while 2023 was 81.82% with a Kappa coefficient of 0.6034 for the land use land cover classes revealing that the classification is accurate. The study revealed that LULCCs are driven by settlement, poverty and climate change mostly thus affecting cropland vegetation and soil fertility majorly. The study concluded that LULCC drivers varied from location to location even within the sub county. The study recommends creation of awareness of understanding the various drivers of LULCCs and impact of each on land quality.

Land use Land cover change, Drivers, Land use Land cover and change detection

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